Doc AWS SDK 예제 GitHub 리포지토리에서 더 많은 SDK 예제를 사용할 수 있습니다. [AWS](https://github.com/awsdocs/aws-doc-sdk-examples) 기계 번역으로 제공되는 번역입니다. 제공된 번역과 원본 영어의 내용이 상충하는 경우에는 영어 버전이 우선합니다. # AWS SDKs 코드 예제 다음 코드 예제에서는 Amazon Bedrock Agents를 AWS 소프트웨어 개발 키트(SDK)와 함께 사용하는 방법을 보여줍니다. *작업*은 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 작업은 개별 서비스 함수를 직접 호출하는 방법을 보여주며, 관련 시나리오의 컨텍스트에 맞는 작업을 볼 수 있습니다. *시나리오*는 동일한 서비스 내에서 또는 다른 AWS 서비스와 결합된 상태에서 여러 함수를 직접적으로 호출하여 특정 태스크를 수행하는 방법을 보여주는 코드 예제입니다. **추가 리소스** + **[ Amazon Bedrock Agents 사용 설명서](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html)** - Amazon Bedrock Agents에 대한 자세한 정보입니다. + **[Amazon Bedrock Agents API 참조](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Agents_for_Amazon_Bedrock.html)** - 사용 가능한 모든 Amazon Bedrock Agents 작업에 대한 세부 정보입니다. + **[AWS 개발자 센터](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23bedrock-agents)** - 범주 또는 전체 텍스트 검색을 기준으로 필터링할 수 있는 코드 예제입니다. + **[AWS SDK 예제](https://github.com/awsdocs/aws-doc-sdk-examples)** - 기본 언어로 된 전체 코드가 포함된 GitHub 리포지토리. 코드 설정 및 실행을 위한 지침이 포함되어 있습니다. **Contents** + [기본 사항](bedrock-agent_code_examples_basics.md) + [Amazon Bedrock Agents 시작](bedrock-agent_example_bedrock-agent_Hello_section.md) + [작업](bedrock-agent_code_examples_actions.md) + [`CreateAgent`](bedrock-agent_example_bedrock-agent_CreateAgent_section.md) + [`CreateAgentActionGroup`](bedrock-agent_example_bedrock-agent_CreateAgentActionGroup_section.md) + [`CreateAgentAlias`](bedrock-agent_example_bedrock-agent_CreateAgentAlias_section.md) + [`CreateFlow`](bedrock-agent_example_bedrock-agent_CreateFlow_section.md) + [`CreateFlowAlias`](bedrock-agent_example_bedrock-agent_CreateFlowAlias_section.md) + [`CreateFlowVersion`](bedrock-agent_example_bedrock-agent_CreateFlowVersion_section.md) + [`CreateKnowledgeBase`](bedrock-agent_example_bedrock-agent_CreateKnowledgeBase_section.md) + [`CreatePrompt`](bedrock-agent_example_bedrock-agent_CreatePrompt_section.md) + [`CreatePromptVersion`](bedrock-agent_example_bedrock-agent_CreatePromptVersion_section.md) + [`DeleteAgent`](bedrock-agent_example_bedrock-agent_DeleteAgent_section.md) + [`DeleteAgentAlias`](bedrock-agent_example_bedrock-agent_DeleteAgentAlias_section.md) + [`DeleteFlow`](bedrock-agent_example_bedrock-agent_DeleteFlow_section.md) + [`DeleteFlowAlias`](bedrock-agent_example_bedrock-agent_DeleteFlowAlias_section.md) + [`DeleteFlowVersion`](bedrock-agent_example_bedrock-agent_DeleteFlowVersion_section.md) + [`DeleteKnowledgeBase`](bedrock-agent_example_bedrock-agent_DeleteKnowledgeBase_section.md) + [`DeletePrompt`](bedrock-agent_example_bedrock-agent_DeletePrompt_section.md) + [`GetAgent`](bedrock-agent_example_bedrock-agent_GetAgent_section.md) + [`GetFlow`](bedrock-agent_example_bedrock-agent_GetFlow_section.md) + [`GetFlowVersion`](bedrock-agent_example_bedrock-agent_GetFlowVersion_section.md) + [`GetKnowledgeBase`](bedrock-agent_example_bedrock-agent_GetKnowledgeBase_section.md) + [`GetPrompt`](bedrock-agent_example_bedrock-agent_GetPrompt_section.md) + [`ListAgentActionGroups`](bedrock-agent_example_bedrock-agent_ListAgentActionGroups_section.md) + [`ListAgentKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListAgentKnowledgeBases_section.md) + [`ListAgents`](bedrock-agent_example_bedrock-agent_ListAgents_section.md) + [`ListFlowAliases`](bedrock-agent_example_bedrock-agent_ListFlowAliases_section.md) + [`ListFlowVersions`](bedrock-agent_example_bedrock-agent_ListFlowVersions_section.md) + [`ListFlows`](bedrock-agent_example_bedrock-agent_ListFlows_section.md) + [`ListKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListKnowledgeBases_section.md) + [`ListPrompts`](bedrock-agent_example_bedrock-agent_ListPrompts_section.md) + [`PrepareAgent`](bedrock-agent_example_bedrock-agent_PrepareAgent_section.md) + [`PrepareFlow`](bedrock-agent_example_bedrock-agent_PrepareFlow_section.md) + [`UpdateFlow`](bedrock-agent_example_bedrock-agent_UpdateFlow_section.md) + [`UpdateFlowAlias`](bedrock-agent_example_bedrock-agent_UpdateFlowAlias_section.md) + [`UpdateKnowledgeBase`](bedrock-agent_example_bedrock-agent_UpdateKnowledgeBase_section.md) + [시나리오](bedrock-agent_code_examples_scenarios.md) + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) + [관리형 프롬프트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockPrompts_section.md) + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) + [Step Functions를 사용하여 생성형 AI 애플리케이션 오케스트레이션](bedrock-agent_example_cross_ServerlessPromptChaining_section.md) # AWS SDKs를 사용하는 Amazon Bedrock Agents의 기본 예제 다음 코드 예제에서는 AWS SDK와 함께 Amazon Bedrock Agents의 기본 사항을 사용하는 방법을 보여줍니다. **Contents** + [Amazon Bedrock Agents 시작](bedrock-agent_example_bedrock-agent_Hello_section.md) + [작업](bedrock-agent_code_examples_actions.md) + [`CreateAgent`](bedrock-agent_example_bedrock-agent_CreateAgent_section.md) + [`CreateAgentActionGroup`](bedrock-agent_example_bedrock-agent_CreateAgentActionGroup_section.md) + [`CreateAgentAlias`](bedrock-agent_example_bedrock-agent_CreateAgentAlias_section.md) + [`CreateFlow`](bedrock-agent_example_bedrock-agent_CreateFlow_section.md) + [`CreateFlowAlias`](bedrock-agent_example_bedrock-agent_CreateFlowAlias_section.md) + [`CreateFlowVersion`](bedrock-agent_example_bedrock-agent_CreateFlowVersion_section.md) + [`CreateKnowledgeBase`](bedrock-agent_example_bedrock-agent_CreateKnowledgeBase_section.md) + [`CreatePrompt`](bedrock-agent_example_bedrock-agent_CreatePrompt_section.md) + [`CreatePromptVersion`](bedrock-agent_example_bedrock-agent_CreatePromptVersion_section.md) + [`DeleteAgent`](bedrock-agent_example_bedrock-agent_DeleteAgent_section.md) + [`DeleteAgentAlias`](bedrock-agent_example_bedrock-agent_DeleteAgentAlias_section.md) + [`DeleteFlow`](bedrock-agent_example_bedrock-agent_DeleteFlow_section.md) + [`DeleteFlowAlias`](bedrock-agent_example_bedrock-agent_DeleteFlowAlias_section.md) + [`DeleteFlowVersion`](bedrock-agent_example_bedrock-agent_DeleteFlowVersion_section.md) + [`DeleteKnowledgeBase`](bedrock-agent_example_bedrock-agent_DeleteKnowledgeBase_section.md) + [`DeletePrompt`](bedrock-agent_example_bedrock-agent_DeletePrompt_section.md) + [`GetAgent`](bedrock-agent_example_bedrock-agent_GetAgent_section.md) + [`GetFlow`](bedrock-agent_example_bedrock-agent_GetFlow_section.md) + [`GetFlowVersion`](bedrock-agent_example_bedrock-agent_GetFlowVersion_section.md) + [`GetKnowledgeBase`](bedrock-agent_example_bedrock-agent_GetKnowledgeBase_section.md) + [`GetPrompt`](bedrock-agent_example_bedrock-agent_GetPrompt_section.md) + [`ListAgentActionGroups`](bedrock-agent_example_bedrock-agent_ListAgentActionGroups_section.md) + [`ListAgentKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListAgentKnowledgeBases_section.md) + [`ListAgents`](bedrock-agent_example_bedrock-agent_ListAgents_section.md) + [`ListFlowAliases`](bedrock-agent_example_bedrock-agent_ListFlowAliases_section.md) + [`ListFlowVersions`](bedrock-agent_example_bedrock-agent_ListFlowVersions_section.md) + [`ListFlows`](bedrock-agent_example_bedrock-agent_ListFlows_section.md) + [`ListKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListKnowledgeBases_section.md) + [`ListPrompts`](bedrock-agent_example_bedrock-agent_ListPrompts_section.md) + [`PrepareAgent`](bedrock-agent_example_bedrock-agent_PrepareAgent_section.md) + [`PrepareFlow`](bedrock-agent_example_bedrock-agent_PrepareFlow_section.md) + [`UpdateFlow`](bedrock-agent_example_bedrock-agent_UpdateFlow_section.md) + [`UpdateFlowAlias`](bedrock-agent_example_bedrock-agent_UpdateFlowAlias_section.md) + [`UpdateKnowledgeBase`](bedrock-agent_example_bedrock-agent_UpdateKnowledgeBase_section.md) # Amazon Bedrock Agents 시작 다음 코드 예제에서는 Amazon Bedrock Agents 사용을 시작하는 방법을 보여줍니다. ------ #### [ JavaScript ] **SDK for JavaScript (v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예제 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요. ``` import { fileURLToPath } from "node:url"; import { BedrockAgentClient, GetAgentCommand, paginateListAgents, } from "@aws-sdk/client-bedrock-agent"; /** * @typedef {Object} AgentSummary */ /** * A simple scenario to demonstrate basic setup and interaction with the Bedrock Agents Client. * * This function first initializes the Amazon Bedrock Agents client for a specific region. * It then retrieves a list of existing agents using the streamlined paginator approach. * For each agent found, it retrieves detailed information using a command object. * * Demonstrates: * - Use of the Bedrock Agents client to initialize and communicate with the AWS service. * - Listing resources in a paginated response pattern. * - Accessing an individual resource using a command object. * * @returns {Promise} A promise that resolves when the function has completed execution. */ export const main = async () => { const region = "us-east-1"; console.log("=".repeat(68)); console.log(`Initializing Amazon Bedrock Agents client for ${region}...`); const client = new BedrockAgentClient({ region }); console.log("Retrieving the list of existing agents..."); const paginatorConfig = { client }; const pages = paginateListAgents(paginatorConfig, {}); /** @type {AgentSummary[]} */ const agentSummaries = []; for await (const page of pages) { agentSummaries.push(...page.agentSummaries); } console.log(`Found ${agentSummaries.length} agents in ${region}.`); if (agentSummaries.length > 0) { for (const agentSummary of agentSummaries) { const agentId = agentSummary.agentId; console.log("=".repeat(68)); console.log(`Retrieving agent with ID: ${agentId}:`); console.log("-".repeat(68)); const command = new GetAgentCommand({ agentId }); const response = await client.send(command); const agent = response.agent; console.log(` Name: ${agent.agentName}`); console.log(` Status: ${agent.agentStatus}`); console.log(` ARN: ${agent.agentArn}`); console.log(` Foundation model: ${agent.foundationModel}`); } } console.log("=".repeat(68)); }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { await main(); } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 다음 주제를 참조하세요. + [GetAgent](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/GetAgentCommand) + [ListAgents](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/ListAgentsCommand) ------ # AWS SDKs를 사용하는 Amazon Bedrock Agents에 대한 작업 다음 코드 예제에서는 AWS SDKs를 사용하여 개별 Amazon Bedrock Agents 작업을 수행하는 방법을 보여줍니다. 각 예제에는 GitHub에 대한 링크가 포함되어 있습니다. 여기에서 코드 설정 및 실행에 대한 지침을 찾을 수 있습니다. 이들 발췌문은 Amazon Bedrock Agents API를 직접적으로 호출하며, 컨텍스트에서 실행되어야 하는 더 큰 프로그램에서 발췌한 코드입니다. [AWS SDKs를 사용하는 Amazon Bedrock 에이전트에 대한 시나리오](bedrock-agent_code_examples_scenarios.md)에서 컨텍스트에 맞는 작업을 볼 수 있습니다. 다음 예제에는 가장 일반적으로 사용되는 작업만 포함되어 있습니다. 전체 목록은 [Amazon Bedrock Agents API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Agents_for_Amazon_Bedrock.html)를 참조하세요. **Topics** + [`CreateAgent`](bedrock-agent_example_bedrock-agent_CreateAgent_section.md) + [`CreateAgentActionGroup`](bedrock-agent_example_bedrock-agent_CreateAgentActionGroup_section.md) + [`CreateAgentAlias`](bedrock-agent_example_bedrock-agent_CreateAgentAlias_section.md) + [`CreateFlow`](bedrock-agent_example_bedrock-agent_CreateFlow_section.md) + [`CreateFlowAlias`](bedrock-agent_example_bedrock-agent_CreateFlowAlias_section.md) + [`CreateFlowVersion`](bedrock-agent_example_bedrock-agent_CreateFlowVersion_section.md) + [`CreateKnowledgeBase`](bedrock-agent_example_bedrock-agent_CreateKnowledgeBase_section.md) + [`CreatePrompt`](bedrock-agent_example_bedrock-agent_CreatePrompt_section.md) + [`CreatePromptVersion`](bedrock-agent_example_bedrock-agent_CreatePromptVersion_section.md) + [`DeleteAgent`](bedrock-agent_example_bedrock-agent_DeleteAgent_section.md) + [`DeleteAgentAlias`](bedrock-agent_example_bedrock-agent_DeleteAgentAlias_section.md) + [`DeleteFlow`](bedrock-agent_example_bedrock-agent_DeleteFlow_section.md) + [`DeleteFlowAlias`](bedrock-agent_example_bedrock-agent_DeleteFlowAlias_section.md) + [`DeleteFlowVersion`](bedrock-agent_example_bedrock-agent_DeleteFlowVersion_section.md) + [`DeleteKnowledgeBase`](bedrock-agent_example_bedrock-agent_DeleteKnowledgeBase_section.md) + [`DeletePrompt`](bedrock-agent_example_bedrock-agent_DeletePrompt_section.md) + [`GetAgent`](bedrock-agent_example_bedrock-agent_GetAgent_section.md) + [`GetFlow`](bedrock-agent_example_bedrock-agent_GetFlow_section.md) + [`GetFlowVersion`](bedrock-agent_example_bedrock-agent_GetFlowVersion_section.md) + [`GetKnowledgeBase`](bedrock-agent_example_bedrock-agent_GetKnowledgeBase_section.md) + [`GetPrompt`](bedrock-agent_example_bedrock-agent_GetPrompt_section.md) + [`ListAgentActionGroups`](bedrock-agent_example_bedrock-agent_ListAgentActionGroups_section.md) + [`ListAgentKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListAgentKnowledgeBases_section.md) + [`ListAgents`](bedrock-agent_example_bedrock-agent_ListAgents_section.md) + [`ListFlowAliases`](bedrock-agent_example_bedrock-agent_ListFlowAliases_section.md) + [`ListFlowVersions`](bedrock-agent_example_bedrock-agent_ListFlowVersions_section.md) + [`ListFlows`](bedrock-agent_example_bedrock-agent_ListFlows_section.md) + [`ListKnowledgeBases`](bedrock-agent_example_bedrock-agent_ListKnowledgeBases_section.md) + [`ListPrompts`](bedrock-agent_example_bedrock-agent_ListPrompts_section.md) + [`PrepareAgent`](bedrock-agent_example_bedrock-agent_PrepareAgent_section.md) + [`PrepareFlow`](bedrock-agent_example_bedrock-agent_PrepareFlow_section.md) + [`UpdateFlow`](bedrock-agent_example_bedrock-agent_UpdateFlow_section.md) + [`UpdateFlowAlias`](bedrock-agent_example_bedrock-agent_UpdateFlowAlias_section.md) + [`UpdateKnowledgeBase`](bedrock-agent_example_bedrock-agent_UpdateKnowledgeBase_section.md) # AWS SDK와 `CreateAgent` 함께 사용 다음 코드 예시는 `CreateAgent`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ JavaScript ] **SDK for JavaScript(v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 생성합니다. ``` import { fileURLToPath } from "node:url"; import { checkForPlaceholders } from "../lib/utils.js"; import { BedrockAgentClient, CreateAgentCommand, } from "@aws-sdk/client-bedrock-agent"; /** * Creates an Amazon Bedrock Agent. * * @param {string} agentName - A name for the agent that you create. * @param {string} foundationModel - The foundation model to be used by the agent you create. * @param {string} agentResourceRoleArn - The ARN of the IAM role with permissions required by the agent. * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An object containing details of the created agent. */ export const createAgent = async ( agentName, foundationModel, agentResourceRoleArn, region = "us-east-1", ) => { const client = new BedrockAgentClient({ region }); const command = new CreateAgentCommand({ agentName, foundationModel, agentResourceRoleArn, }); const response = await client.send(command); return response.agent; }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { // Replace the placeholders for agentName and accountId, and roleName with a unique name for the new agent, // the id of your AWS account, and the name of an existing execution role that the agent can use inside your account. // For foundationModel, specify the desired model. Ensure to remove the brackets '[]' before adding your data. // A string (max 100 chars) that can include letters, numbers, dashes '-', and underscores '_'. const agentName = "[your-bedrock-agent-name]"; // Your AWS account id. const accountId = "[123456789012]"; // The name of the agent's execution role. It must be prefixed by `AmazonBedrockExecutionRoleForAgents_`. const roleName = "[AmazonBedrockExecutionRoleForAgents_your-role-name]"; // The ARN for the agent's execution role. // Follow the ARN format: 'arn:aws:iam::account-id:role/role-name' const roleArn = `arn:aws:iam::${accountId}:role/${roleName}`; // Specify the model for the agent. Change if a different model is preferred. const foundationModel = "anthropic.claude-v2"; // Check for unresolved placeholders in agentName and roleArn. checkForPlaceholders([agentName, roleArn]); console.log("Creating a new agent..."); const agent = await createAgent(agentName, foundationModel, roleArn); console.log(agent); } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 [CreateAgent](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/CreateAgentCommand)를 참조하세요. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 생성합니다. ``` def create_agent(self, agent_name, foundation_model, role_arn, instruction): """ Creates an agent that orchestrates interactions between foundation models, data sources, software applications, user conversations, and APIs to carry out tasks to help customers. :param agent_name: A name for the agent. :param foundation_model: The foundation model to be used for orchestration by the agent. :param role_arn: The ARN of the IAM role with permissions needed by the agent. :param instruction: Instructions that tell the agent what it should do and how it should interact with users. :return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception. """ try: response = self.client.create_agent( agentName=agent_name, foundationModel=foundation_model, agentResourceRoleArn=role_arn, instruction=instruction, ) except ClientError as e: logger.error(f"Error: Couldn't create agent. Here's why: {e}") raise else: return response["agent"] ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [CreateAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgent)를 참조하세요. ------ # AWS SDK와 `CreateAgentActionGroup` 함께 사용 다음 코드 예시는 `CreateAgentActionGroup`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트 작업 그룹을 생성합니다. ``` def create_agent_action_group( self, name, description, agent_id, agent_version, function_arn, api_schema ): """ Creates an action group for an agent. An action group defines a set of actions that an agent should carry out for the customer. :param name: The name to give the action group. :param description: The description of the action group. :param agent_id: The unique identifier of the agent for which to create the action group. :param agent_version: The version of the agent for which to create the action group. :param function_arn: The ARN of the Lambda function containing the business logic that is carried out upon invoking the action. :param api_schema: Contains the OpenAPI schema for the action group. :return: Details about the action group that was created. """ try: response = self.client.create_agent_action_group( actionGroupName=name, description=description, agentId=agent_id, agentVersion=agent_version, actionGroupExecutor={"lambda": function_arn}, apiSchema={"payload": api_schema}, ) agent_action_group = response["agentActionGroup"] except ClientError as e: logger.error(f"Error: Couldn't create agent action group. Here's why: {e}") raise else: return agent_action_group ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [CreateAgentActionGroup](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentActionGroup)을 참조하세요. ------ # AWS SDK와 `CreateAgentAlias` 함께 사용 다음 코드 예시는 `CreateAgentAlias`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트 별칭을 생성합니다. ``` def create_agent_alias(self, name, agent_id): """ Creates an alias of an agent that can be used to deploy the agent. :param name: The name of the alias. :param agent_id: The unique identifier of the agent. :return: Details about the alias that was created. """ try: response = self.client.create_agent_alias( agentAliasName=name, agentId=agent_id ) agent_alias = response["agentAlias"] except ClientError as e: logger.error(f"Couldn't create agent alias. {e}") raise else: return agent_alias ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [CreateAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentAlias)를 참조하세요. ------ # AWS SDK와 `CreateFlow` 함께 사용 다음 코드 예시는 `CreateFlow`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 생성합니다. ``` def create_flow(client, flow_name, flow_description, role_arn, flow_def): """ Creates an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_name (str): The name for the new flow. role_arn (str): The ARN for the IAM role that use flow uses. flow_def (json): The JSON definition of the flow that you want to create. Returns: dict: The response from CreateFlow. """ try: logger.info("Creating flow: %s.", flow_name) response = client.create_flow( name=flow_name, description=flow_description, executionRoleArn=role_arn, definition=flow_def ) logger.info("Successfully created flow: %s. ID: %s", flow_name, {response['id']}) return response except ClientError as e: logger.exception("Client error creating flow: %s", {str(e)}) raise except Exception as e: logger.exception("Unexepcted error creating flow: %s", {str(e)}) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlow)를 참조하세요. ------ # AWS SDK와 `CreateFlowAlias` 함께 사용 다음 코드 예시는 `CreateFlowAlias`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름의 별칭을 생성합니다. ``` def create_flow_alias(client, flow_id, flow_version, name, description): """ Creates an alias for an Amazon Bedrock flow. Args: client: bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: str: The ID for the flow alias. """ try: logger.info("Creating flow alias for flow: %s.", flow_id) response = client.create_flow_alias( flowIdentifier=flow_id, name=name, description=description, routingConfiguration=[ { "flowVersion": flow_version } ] ) logger.info("Successfully created flow alias for %s.", flow_id) return response['id'] except ClientError as e: logging.exception("Client error creating alias for flow: %s - %s", flow_id, str(e)) raise except Exception as e: logging.exception("Unexpected error creating alias for flow : %s - %s", flow_id, str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowAlias)를 참조하세요. ------ # AWS SDK와 `CreateFlowVersion` 함께 사용 다음 코드 예시는 `CreateFlowVersion`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름 버전을 생성합니다. ``` def create_flow_version(client, flow_id, description): """ Creates a version of an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow. description (str) : A description for the flow. Returns: str: The version for the flow. """ try: logger.info("Creating flow version for flow: %s.", flow_id) # Call CreateFlowVersion operation response = client.create_flow_version( flowIdentifier=flow_id, description=description ) logging.info("Successfully created flow version %s for flow %s.", response['version'], flow_id) return response['version'] except ClientError as e: logging.exception("Client error creating flow: %s", str(e)) raise except Exception as e: logging.exception("Unexpected error creating flow : %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreateFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowVersion)을 참조하세요. ------ # AWS SDK와 `CreateKnowledgeBase` 함께 사용 다음 코드 예시는 `CreateKnowledgeBase`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 지식 기반을 생성합니다. ``` def create_knowledge_base(bedrock_agent_client, name, role_arn, description=None): """ Creates a new knowledge base. Args: bedrock_agent_client: The Boto3 Bedrock Agent client. name (str): The name of the knowledge base. role_arn (str): The ARN of the IAM role that the knowledge base assumes to access resources. description (str, optional): A description of the knowledge base. Returns: dict: The details of the created knowledge base. """ try: kwargs = { "name": name, "roleArn": role_arn, "knowledgeBaseConfiguration": { "type": "VECTOR", "vectorKnowledgeBaseConfiguration": { "embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1" } }, "storageConfiguration": { "type": "OPENSEARCH_SERVERLESS", # Note: You will need to create an OpenSearch Serverless collection first and replace this ARN # with your actual collection ARN from the OpenSearch console. If you use the console instead, # you can use the quick-create flow to have Knowledge Bases create the collection for you. "opensearchServerlessConfiguration": { "collectionArn": "arn:aws:aoss:us-east-1::123456789012:collection/abcdefgh12345678defgh", "fieldMapping": { "metadataField": "metadata", "textField": "text", "vectorField": "vector" }, "vectorIndexName": "test-uuid" }, }, "clientToken": "test-client-token-" + str(uuid.uuid4()) } if description: kwargs["description"] = description response = bedrock_agent_client.create_knowledge_base(**kwargs) logger.info("Created knowledge base with ID: %s", response["knowledgeBase"]["knowledgeBaseId"]) return response["knowledgeBase"] except ClientError as err: logger.error( "Couldn't create knowledge base. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreateKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateKnowledgeBase)를 참조하세요. ------ # AWS SDK와 `CreatePrompt` 함께 사용 다음 코드 예시는 `CreatePrompt`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [관리형 프롬프트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockPrompts_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 관리형 프롬프트를 생성합니다. ``` def create_prompt(client, prompt_name, prompt_description, prompt_template, model_id=None): """ Creates an Amazon Bedrock managed prompt. Args: client: Amazon Bedrock Agent boto3 client. prompt_name (str): The name for the new prompt. prompt_description (str): The description for the new prompt. prompt_template (str): The template for the prompt. model_id (str, optional): The model ID to associate with the prompt. Returns: dict: The response from CreatePrompt. """ try: logger.info("Creating prompt: %s.", prompt_name) # Create a variant with the template variant = { "name": "default", "templateType": "TEXT", "templateConfiguration": { "text": { "text": prompt_template, "inputVariables": [] } } } # Extract input variables from the template # Look for patterns like {{variable_name}} variables = re.findall(r'{{(.*?)}}', prompt_template) for var in variables: variant["templateConfiguration"]["text"]["inputVariables"].append({"name": var.strip()}) # Add model ID if provided if model_id: variant["modelId"] = model_id # Create the prompt with the variant create_params = { 'name': prompt_name, 'description': prompt_description, 'variants': [variant] } response = client.create_prompt(**create_params) logger.info("Successfully created prompt: %s. ID: %s", prompt_name, response['id']) return response except ClientError as e: logger.exception("Client error creating prompt: %s", str(e)) raise except Exception as e: logger.exception("Unexpected error creating prompt: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreatePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePrompt)를 참조하세요. ------ # AWS SDK와 `CreatePromptVersion` 함께 사용 다음 코드 예시는 `CreatePromptVersion`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [관리형 프롬프트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockPrompts_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 관리형 프롬프트 버전을 생성합니다. ``` def create_prompt_version(client, prompt_id, description=None): """ Creates a version of an Amazon Bedrock managed prompt. Args: client: Amazon Bedrock Agent boto3 client. prompt_id (str): The identifier of the prompt to create a version for. description (str, optional): A description for the version. Returns: dict: The response from CreatePromptVersion. """ try: logger.info("Creating version for prompt ID: %s.", prompt_id) create_params = { 'promptIdentifier': prompt_id } if description: create_params['description'] = description response = client.create_prompt_version(**create_params) logger.info("Successfully created prompt version: %s", response['version']) logger.info("Prompt version ARN: %s", response['arn']) return response except ClientError as e: logger.exception("Client error creating prompt version: %s", str(e)) raise except Exception as e: logger.exception("Unexpected error creating prompt version: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [CreatePromptVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePromptVersion)을 참조하세요. ------ # AWS SDK와 `DeleteAgent` 함께 사용 다음 코드 예시는 `DeleteAgent`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ JavaScript ] **SDK for JavaScript(v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 삭제합니다. ``` import { fileURLToPath } from "node:url"; import { checkForPlaceholders } from "../lib/utils.js"; import { BedrockAgentClient, DeleteAgentCommand, } from "@aws-sdk/client-bedrock-agent"; /** * Deletes an Amazon Bedrock Agent. * * @param {string} agentId - The unique identifier of the agent to delete. * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An object containing the agent id, the status, and some additional metadata. */ export const deleteAgent = (agentId, region = "us-east-1") => { const client = new BedrockAgentClient({ region }); const command = new DeleteAgentCommand({ agentId }); return client.send(command); }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { // Replace the placeholders for agentId with an existing agent's id. // Ensure to remove the brackets (`[]`) before adding your data. // The agentId must be an alphanumeric string with exactly 10 characters. const agentId = "[ABC123DE45]"; // Check for unresolved placeholders in agentId. checkForPlaceholders([agentId]); console.log(`Deleting agent with ID ${agentId}...`); const response = await deleteAgent(agentId); console.log(response); } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 [DeleteAgent](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/DeleteAgentCommand)를 참조하세요. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 삭제합니다. ``` def delete_agent(self, agent_id): """ Deletes an Amazon Bedrock agent. :param agent_id: The unique identifier of the agent to delete. :return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception. """ try: response = self.client.delete_agent( agentId=agent_id, skipResourceInUseCheck=False ) except ClientError as e: logger.error(f"Couldn't delete agent. {e}") raise else: return response ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [DeleteAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgent)를 참조하세요. ------ # AWS SDK와 `DeleteAgentAlias` 함께 사용 다음 코드 예시는 `DeleteAgentAlias`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트 별칭을 삭제합니다. ``` def delete_agent_alias(self, agent_id, agent_alias_id): """ Deletes an alias of an Amazon Bedrock agent. :param agent_id: The unique identifier of the agent that the alias belongs to. :param agent_alias_id: The unique identifier of the alias to delete. :return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception. """ try: response = self.client.delete_agent_alias( agentId=agent_id, agentAliasId=agent_alias_id ) except ClientError as e: logger.error(f"Couldn't delete agent alias. {e}") raise else: return response ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [DeleteAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgentAlias)를 참조하세요. ------ # AWS SDK와 `DeleteFlow` 함께 사용 다음 코드 예시는 `DeleteFlow`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 삭제합니다. ``` def delete_flow(client, flow_id): """ Deletes an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow that you want to delete. Returns: dict: The response from the DeleteFLow operation. """ try: logger.info("Deleting flow ID: %s.", flow_id) # Call DeleteFlow operation response = client.delete_flow( flowIdentifier=flow_id, skipResourceInUseCheck=True ) logger.info("Finished deleting flow ID: %s", flow_id) return response except ClientError as e: logger.exception("Client error deleting flow: %s", {str(e)}) raise except Exception as e: logger.exception("Unexepcted error deleting flow: %s", {str(e)}) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [DeleteFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlow)를 참조하세요. ------ # AWS SDK와 `DeleteFlowAlias` 함께 사용 다음 코드 예시는 `DeleteFlowAlias`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름의 별칭을 삭제합니다. ``` def delete_flow_alias(client, flow_id, flow_alias_id): """ Deletes an Amazon Bedrock flow alias. Args: client: bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: dict: The response from the call to DetectFLowAlias """ try: logger.info("Deleting flow alias %s for flow: %s.", flow_alias_id, flow_id) # Delete the flow alias. response = client.delete_flow_alias( aliasIdentifier=flow_alias_id, flowIdentifier=flow_id ) logging.info("Successfully deleted flow version for %s.", flow_id) return response except ClientError as e: logging.exception("Client error deleting flow version: %s", str(e)) raise except Exception as e: logging.exception("Unexpected deleting flow version: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [DeleteFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowAlias)를 참조하세요. ------ # AWS SDK와 `DeleteFlowVersion` 함께 사용 다음 코드 예시는 `DeleteFlowVersion`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름 버전을 삭제합니다. ``` def delete_flow_version(client, flow_id, flow_version): """ Deletes a version of an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: dict: The response from DeleteFlowVersion. """ try: logger.info("Deleting flow version %s for flow: %s.",flow_version, flow_id) # Call DeleteFlowVersion operation response = client.delete_flow_version( flowIdentifier=flow_id, flowVersion=flow_version ) logging.info("Successfully deleted flow version %s for %s.", flow_version, flow_id) return response except ClientError as e: logging.exception("Client error deleting flow version: %s ", str(e)) raise except Exception as e: logging.exception("Unexpected deleting flow version: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [DeleteFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowVersion)을 참조하세요. ------ # AWS SDK와 `DeleteKnowledgeBase` 함께 사용 다음 코드 예시는 `DeleteKnowledgeBase`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 지식 기반을 삭제합니다. ``` def delete_knowledge_base(bedrock_agent_client, knowledge_base_id): """ Deletes a knowledge base. Args: bedrock_agent_client: The Boto3 Bedrock Agent client. knowledge_base_id (str): The ID of the knowledge base to delete. Returns: bool: True if the deletion was successful. """ try: bedrock_agent_client.delete_knowledge_base( knowledgeBaseId=knowledge_base_id ) logger.info("Deleted knowledge base: %s", knowledge_base_id) return True except ClientError as err: logger.error( "Couldn't delete knowledge base %s. Here's why: %s: %s", knowledge_base_id, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [DeleteKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteKnowledgeBase)를 참조하세요. ------ # AWS SDK와 `DeletePrompt` 함께 사용 다음 코드 예시는 `DeletePrompt`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [관리형 프롬프트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockPrompts_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 관리형 프롬프트를 삭제합니다. ``` def delete_prompt(client, prompt_id): """ Deletes an Amazon Bedrock managed prompt. Args: client: Amazon Bedrock Agent boto3 client. prompt_id (str): The identifier of the prompt that you want to delete. Returns: dict: The response from the DeletePrompt operation. """ try: logger.info("Deleting prompt ID: %s.", prompt_id) response = client.delete_prompt( promptIdentifier=prompt_id ) logger.info("Finished deleting prompt ID: %s", prompt_id) return response except ClientError as e: logger.exception("Client error deleting prompt: %s", str(e)) raise except Exception as e: logger.exception("Unexpected error deleting prompt: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [DeletePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeletePrompt)를 참조하세요. ------ # AWS SDK와 `GetAgent` 함께 사용 다음 코드 예시는 `GetAgent`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ JavaScript ] **SDK for JavaScript(v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 가져옵니다. ``` import { fileURLToPath } from "node:url"; import { checkForPlaceholders } from "../lib/utils.js"; import { BedrockAgentClient, GetAgentCommand, } from "@aws-sdk/client-bedrock-agent"; /** * Retrieves the details of an Amazon Bedrock Agent. * * @param {string} agentId - The unique identifier of the agent. * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An object containing the agent details. */ export const getAgent = async (agentId, region = "us-east-1") => { const client = new BedrockAgentClient({ region }); const command = new GetAgentCommand({ agentId }); const response = await client.send(command); return response.agent; }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { // Replace the placeholders for agentId with an existing agent's id. // Ensure to remove the brackets '[]' before adding your data. // The agentId must be an alphanumeric string with exactly 10 characters. const agentId = "[ABC123DE45]"; // Check for unresolved placeholders in agentId. checkForPlaceholders([agentId]); console.log(`Retrieving agent with ID ${agentId}...`); const agent = await getAgent(agentId); console.log(agent); } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 [GetAgent](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/GetAgentCommand)를 참조하세요. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 가져옵니다. ``` def get_agent(self, agent_id, log_error=True): """ Gets information about an agent. :param agent_id: The unique identifier of the agent. :param log_error: Whether to log any errors that occur when getting the agent. If True, errors will be logged to the logger. If False, errors will still be raised, but not logged. :return: The information about the requested agent. """ try: response = self.client.get_agent(agentId=agent_id) agent = response["agent"] except ClientError as e: if log_error: logger.error(f"Couldn't get agent {agent_id}. {e}") raise else: return agent ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [GetAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetAgent)를 참조하세요. ------ # AWS SDK와 `GetFlow` 함께 사용 다음 코드 예시는 `GetFlow`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 가져옵니다. ``` def get_flow(client, flow_id): """ Gets an Amazon Bedrock flow. Args: client: bedrock agent boto3 client. flow_id (str): The identifier of the flow that you want to get. Returns: dict: The response from the GetFlow operation. """ try: logger.info("Getting flow ID: %s.", flow_id) # Call GetFlow operation. response = client.get_flow( flowIdentifier=flow_id ) logger.info("Retrieved flow ID: %s. Name: %s", flow_id, response['name']) return response except ClientError as e: logger.exception("Client error getting flow: %s", {str(e)}) raise except Exception as e: logger.exception("Unexepcted error getting flow: %s", {str(e)}) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [GetFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlow)를 참조하세요. ------ # AWS SDK와 `GetFlowVersion` 함께 사용 다음 코드 예시는 `GetFlowVersion`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름 버전을 가져옵니다. ``` def get_flow_version(client, flow_id, flow_version): """ Gets information about a version of an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow. flow_version (str): The flow version of the flow. Returns: dict: The response from the call to GetFlowVersion. """ try: logger.info("Deleting flow version for flow: %s.", flow_id) # Call GetFlowVersion operation response = client.get_flow_version( flowIdentifier=flow_id, flowVersion=flow_version ) logging.info("Successfully got flow version %s information for flow %s.", flow_version, flow_id) return response except ClientError as e: logging.exception("Client error getting flow version: %s", str(e)) raise except Exception as e: logging.exception("Unexpected error getting flow version: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [GetFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowVersion)을 참조하세요. ------ # AWS SDK와 `GetKnowledgeBase` 함께 사용 다음 코드 예시는 `GetKnowledgeBase`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 지식 기반을 가져옵니다. ``` def get_knowledge_base(bedrock_agent_client, knowledge_base_id): """ Gets details about a specific knowledge base. Args: bedrock_agent_client: The Boto3 Bedrock Agent client. knowledge_base_id (str): The ID of the knowledge base. Returns: dict: The details of the knowledge base. """ try: response = bedrock_agent_client.get_knowledge_base( knowledgeBaseId=knowledge_base_id ) logger.info("Retrieved knowledge base: %s", knowledge_base_id) return response["knowledgeBase"] except ClientError as err: logger.error( "Couldn't get knowledge base %s. Here's why: %s: %s", knowledge_base_id, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [GetKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetKnowledgeBase)를 참조하세요. ------ # AWS SDK와 `GetPrompt` 함께 사용 다음 코드 예시는 `GetPrompt`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 관리형 프롬프트를 가져옵니다. ``` def get_prompt(client, prompt_id): """ Gets an Amazon Bedrock managed prompt. Args: client: Amazon Bedrock Agent boto3 client. prompt_id (str): The identifier of the prompt that you want to get. Returns: dict: The response from the GetPrompt operation. """ try: logger.info("Getting prompt ID: %s.", prompt_id) response = client.get_prompt( promptIdentifier=prompt_id ) logger.info("Retrieved prompt ID: %s. Name: %s", prompt_id, response['name']) return response except ClientError as e: logger.exception("Client error getting prompt: %s", str(e)) raise except Exception as e: logger.exception("Unexpected error getting prompt: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [GetPrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetPrompt)를 참조하세요. ------ # AWS SDK와 `ListAgentActionGroups` 함께 사용 다음 코드 예시는 `ListAgentActionGroups`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ JavaScript ] **SDK for JavaScript(v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트의 작업 그룹을 나열합니다. ``` import { fileURLToPath } from "node:url"; import { checkForPlaceholders } from "../lib/utils.js"; import { BedrockAgentClient, ListAgentActionGroupsCommand, paginateListAgentActionGroups, } from "@aws-sdk/client-bedrock-agent"; /** * Retrieves a list of Action Groups of an agent utilizing the paginator function. * * This function leverages a paginator, which abstracts the complexity of pagination, providing * a straightforward way to handle paginated results inside a `for await...of` loop. * * @param {string} agentId - The unique identifier of the agent. * @param {string} agentVersion - The version of the agent. * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An array of action group summaries. */ export const listAgentActionGroupsWithPaginator = async ( agentId, agentVersion, region = "us-east-1", ) => { const client = new BedrockAgentClient({ region }); // Create a paginator configuration const paginatorConfig = { client, pageSize: 10, // optional, added for demonstration purposes }; const params = { agentId, agentVersion }; const pages = paginateListAgentActionGroups(paginatorConfig, params); // Paginate until there are no more results const actionGroupSummaries = []; for await (const page of pages) { actionGroupSummaries.push(...page.actionGroupSummaries); } return actionGroupSummaries; }; /** * Retrieves a list of Action Groups of an agent utilizing the ListAgentActionGroupsCommand. * * This function demonstrates the manual approach, sending a command to the client and processing the response. * Pagination must manually be managed. For a simplified approach that abstracts away pagination logic, see * the `listAgentActionGroupsWithPaginator()` example below. * * @param {string} agentId - The unique identifier of the agent. * @param {string} agentVersion - The version of the agent. * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An array of action group summaries. */ export const listAgentActionGroupsWithCommandObject = async ( agentId, agentVersion, region = "us-east-1", ) => { const client = new BedrockAgentClient({ region }); let nextToken; const actionGroupSummaries = []; do { const command = new ListAgentActionGroupsCommand({ agentId, agentVersion, nextToken, maxResults: 10, // optional, added for demonstration purposes }); /** @type {{actionGroupSummaries: ActionGroupSummary[], nextToken?: string}} */ const response = await client.send(command); for (const actionGroup of response.actionGroupSummaries || []) { actionGroupSummaries.push(actionGroup); } nextToken = response.nextToken; } while (nextToken); return actionGroupSummaries; }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { // Replace the placeholders for agentId and agentVersion with an existing agent's id and version. // Ensure to remove the brackets '[]' before adding your data. // The agentId must be an alphanumeric string with exactly 10 characters. const agentId = "[ABC123DE45]"; // A string either containing `DRAFT` or a number with 1-5 digits (e.g., '123' or 'DRAFT'). const agentVersion = "[DRAFT]"; // Check for unresolved placeholders in agentId and agentVersion. checkForPlaceholders([agentId, agentVersion]); console.log("=".repeat(68)); console.log( "Listing agent action groups using ListAgentActionGroupsCommand:", ); for (const actionGroup of await listAgentActionGroupsWithCommandObject( agentId, agentVersion, )) { console.log(actionGroup); } console.log("=".repeat(68)); console.log( "Listing agent action groups using the paginateListAgents function:", ); for (const actionGroup of await listAgentActionGroupsWithPaginator( agentId, agentVersion, )) { console.log(actionGroup); } } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 [ListAgentActionGroups](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/ListAgentActionGroupsCommand)를 참조하세요. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트의 작업 그룹을 나열합니다. ``` def list_agent_action_groups(self, agent_id, agent_version): """ List the action groups for a version of an Amazon Bedrock Agent. :param agent_id: The unique identifier of the agent. :param agent_version: The version of the agent. :return: The list of action group summaries for the version of the agent. """ try: action_groups = [] paginator = self.client.get_paginator("list_agent_action_groups") for page in paginator.paginate( agentId=agent_id, agentVersion=agent_version, PaginationConfig={"PageSize": 10}, ): action_groups.extend(page["actionGroupSummaries"]) except ClientError as e: logger.error(f"Couldn't list action groups. {e}") raise else: return action_groups ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [ListAgentActionGroups](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentActionGroups)를 참조하세요. ------ # AWS SDK와 `ListAgentKnowledgeBases` 함께 사용 다음 코드 예시는 `ListAgentKnowledgeBases`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트와 연결된 지식 기반을 나열합니다. ``` def list_agent_knowledge_bases(self, agent_id, agent_version): """ List the knowledge bases associated with a version of an Amazon Bedrock Agent. :param agent_id: The unique identifier of the agent. :param agent_version: The version of the agent. :return: The list of knowledge base summaries for the version of the agent. """ try: knowledge_bases = [] paginator = self.client.get_paginator("list_agent_knowledge_bases") for page in paginator.paginate( agentId=agent_id, agentVersion=agent_version, PaginationConfig={"PageSize": 10}, ): knowledge_bases.extend(page["agentKnowledgeBaseSummaries"]) except ClientError as e: logger.error(f"Couldn't list knowledge bases. {e}") raise else: return knowledge_bases ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [ListAgentKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentKnowledgeBases)를 참조하세요. ------ # AWS SDK와 `ListAgents` 함께 사용 다음 코드 예시는 `ListAgents`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ JavaScript ] **SDK for JavaScript(v3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 계정에 속한 에이전트를 나열합니다. ``` import { fileURLToPath } from "node:url"; import { BedrockAgentClient, ListAgentsCommand, paginateListAgents, } from "@aws-sdk/client-bedrock-agent"; /** * Retrieves a list of available Amazon Bedrock agents utilizing the paginator function. * * This function leverages a paginator, which abstracts the complexity of pagination, providing * a straightforward way to handle paginated results inside a `for await...of` loop. * * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An array of agent summaries. */ export const listAgentsWithPaginator = async (region = "us-east-1") => { const client = new BedrockAgentClient({ region }); const paginatorConfig = { client, pageSize: 10, // optional, added for demonstration purposes }; const pages = paginateListAgents(paginatorConfig, {}); // Paginate until there are no more results const agentSummaries = []; for await (const page of pages) { agentSummaries.push(...page.agentSummaries); } return agentSummaries; }; /** * Retrieves a list of available Amazon Bedrock agents utilizing the ListAgentsCommand. * * This function demonstrates the manual approach, sending a command to the client and processing the response. * Pagination must manually be managed. For a simplified approach that abstracts away pagination logic, see * the `listAgentsWithPaginator()` example below. * * @param {string} [region='us-east-1'] - The AWS region in use. * @returns {Promise} An array of agent summaries. */ export const listAgentsWithCommandObject = async (region = "us-east-1") => { const client = new BedrockAgentClient({ region }); let nextToken; const agentSummaries = []; do { const command = new ListAgentsCommand({ nextToken, maxResults: 10, // optional, added for demonstration purposes }); /** @type {{agentSummaries: AgentSummary[], nextToken?: string}} */ const paginatedResponse = await client.send(command); agentSummaries.push(...(paginatedResponse.agentSummaries || [])); nextToken = paginatedResponse.nextToken; } while (nextToken); return agentSummaries; }; // Invoke main function if this file was run directly. if (process.argv[1] === fileURLToPath(import.meta.url)) { console.log("=".repeat(68)); console.log("Listing agents using ListAgentsCommand:"); for (const agent of await listAgentsWithCommandObject()) { console.log(agent); } console.log("=".repeat(68)); console.log("Listing agents using the paginateListAgents function:"); for (const agent of await listAgentsWithPaginator()) { console.log(agent); } } ``` + API 세부 정보는 *AWS SDK for JavaScript API 참조*의 [ListAgents](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/bedrock-agent/command/ListAgentsCommand)를 참조하세요. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 계정에 속한 에이전트를 나열합니다. ``` def list_agents(self): """ List the available Amazon Bedrock Agents. :return: The list of available bedrock agents. """ try: all_agents = [] paginator = self.client.get_paginator("list_agents") for page in paginator.paginate(PaginationConfig={"PageSize": 10}): all_agents.extend(page["agentSummaries"]) except ClientError as e: logger.error(f"Couldn't list agents. {e}") raise else: return all_agents ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [ListAgents](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgents)를 참조하세요. ------ # AWS SDK와 `ListFlowAliases` 함께 사용 다음 코드 예시는 `ListFlowAliases`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름의 별칭을 나열합니다. ``` def list_flow_aliases(client, flow_id): """ Lists the aliases of an Amazon Bedrock flow. Args: client: bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: dict: The response from ListFlowAliases. """ try: finished = False logger.info("Listing flow aliases for flow: %s.", flow_id) print(f"Aliases for flow: {flow_id}") response = client.list_flow_aliases( flowIdentifier=flow_id, maxResults=10) while finished is False: for alias in response['flowAliasSummaries']: print(f"Alias Name: {alias['name']}") print(f"ID: {alias['id']}") print(f"Description: {alias.get('description', 'No description')}\n") if 'nextToken' in response: next_token = response['nextToken'] response = client.list_flow_aliases(maxResults=10, nextToken=next_token) else: finished = True logging.info("Successfully listed flow aliases for flow %s.", flow_id) return response except ClientError as e: logging.exception("Client error listing flow aliases: %s", str(e)) raise except Exception as e: logging.exception("Unexpected error listing flow aliases: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [ListFlowAliases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlowAliases)를 참조하세요. ------ # AWS SDK와 `ListFlowVersions` 함께 사용 다음 코드 예시는 `ListFlowVersions`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름 버전을 나열합니다. ``` def list_flow_versions(client, flow_id): """ Lists the versions of an Amazon Bedrock flow. Args: client: Amazon bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: dict: The response from ListFlowVersions. """ try: finished = False logger.info("Listing flow versions for flow: %s.", flow_id) response = client.list_flow_versions( flowIdentifier=flow_id, maxResults=10) while finished is False: print(f"Versions for flow:{flow_id}") for version in response['flowVersionSummaries']: print(f"Version: {version['version']}") print(f"Status: {version['status']}\n") if 'nextToken' in response: next_token = response['nextToken'] response = client.list_flow_versions(maxResults=10, nextToken=next_token) else: finished = True logging.info("Successfully listed flow versions for flow %s.", flow_id) return response except ClientError as e: logging.exception("Client error listing flow versions: %s", str(e)) raise except Exception as e: logging.exception("Unexpected error listing flow versions: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [ListFlowVersions](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlowVersions)을 참조하세요. ------ # AWS SDK와 `ListFlows` 함께 사용 다음 코드 예시는 `ListFlows`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 나열합니다. ``` def list_flows(client): """ Lists versions of an Amazon Bedrock flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: Nothing. """ try: finished = False logger.info("Listing flows:") response = client.list_flows(maxResults=10) while finished is False: for flow in response['flowSummaries']: print(f"ID: {flow['id']}") print(f"Name: {flow['name']}") print( f"Description: {flow.get('description', 'No description')}") print(f"Latest version: {flow['version']}") print(f"Status: {flow['status']}\n") if 'nextToken' in response: next_token = response['nextToken'] response = client.list_flows(maxResults=10, nextToken=next_token) else: finished = True logging.info("Successfully listed flows.") except ClientError as e: logging.exception("Client error listing flow versions: %s", str(e)) raise except Exception as e: logging.exception("Unexpected error listing flow versions: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [ListFlows](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListFlows)를 참조하세요. ------ # AWS SDK와 `ListKnowledgeBases` 함께 사용 다음 코드 예시는 `ListKnowledgeBases`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 지식 기반을 나열합니다. ``` def list_knowledge_bases(bedrock_agent_client, max_results=None): """ Lists the knowledge bases in your AWS account. Args: bedrock_agent_client: The Boto3 Bedrock Agent client. max_results (int, optional): The maximum number of knowledge bases to return. Returns: list: A list of knowledge base details. """ try: kwargs = {} if max_results is not None: kwargs["maxResults"] = max_results # Initialize an empty list to store all knowledge bases all_knowledge_bases = [] # Use paginator to handle pagination automatically paginator = bedrock_agent_client.get_paginator('list_knowledge_bases') page_iterator = paginator.paginate(**kwargs) # Iterate through each page of results for page in page_iterator: all_knowledge_bases.extend(page.get('knowledgeBaseSummaries', [])) logger.info("Found %s knowledge bases.", len(all_knowledge_bases)) return all_knowledge_bases except ClientError as err: logger.error( "Couldn't list knowledge bases. Here's why: %s: %s", err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [ListKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListKnowledgeBases)를 참조하세요. ------ # AWS SDK와 `ListPrompts` 함께 사용 다음 코드 예시는 `ListPrompts`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 관리형 프롬프트를 나열합니다. ``` def list_prompts(client, max_results=10): """ Lists Amazon Bedrock managed prompts. Args: client: Amazon Bedrock Agent boto3 client. max_results (int): Maximum number of results to return per page. Returns: list: A list of prompt summaries. """ try: logger.info("Listing prompts:") # Create a paginator for the list_prompts operation paginator = client.get_paginator('list_prompts') # Create the pagination parameters pagination_config = { 'maxResults': max_results } # Initialize an empty list to store all prompts all_prompts = [] # Iterate through all pages for page in paginator.paginate(**pagination_config): all_prompts.extend(page.get('promptSummaries', [])) logger.info("Successfully listed %s prompts.", len(all_prompts)) return all_prompts except ClientError as e: logger.exception("Client error listing prompts: %s", str(e)) raise except Exception as e: logger.exception("Unexpected error listing prompts: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [ListPrompts](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListPrompts)를 참조하세요. ------ # AWS SDK와 `PrepareAgent` 함께 사용 다음 코드 예시는 `PrepareAgent`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 내부 테스트를 위해 에이전트를 준비합니다. ``` def prepare_agent(self, agent_id): """ Creates a DRAFT version of the agent that can be used for internal testing. :param agent_id: The unique identifier of the agent to prepare. :return: The response from Amazon Bedrock Agents if successful, otherwise raises an exception. """ try: prepared_agent_details = self.client.prepare_agent(agentId=agent_id) except ClientError as e: logger.error(f"Couldn't prepare agent. {e}") raise else: return prepared_agent_details ``` + API 세부 정보는 **AWS SDK for Python(Boto3) API 참조의 [PrepareAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareAgent)를 참조하세요. ------ # AWS SDK와 `PrepareFlow` 함께 사용 다음 코드 예시는 `PrepareFlow`의 사용 방법을 보여 줍니다. 작업 예제는 대규모 프로그램에서 발췌한 코드이며 컨텍스트에 맞춰 실행해야 합니다. 다음 코드 예제에서는 컨텍스트 내에서 이 작업을 확인할 수 있습니다. + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) ------ #### [ Python ] **SDK for Python (Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 준비합니다. ``` def prepare_flow(client, flow_id): """ Prepares an Amazon Bedrock Flow. Args: client: Amazon Bedrock agent boto3 client. flow_id (str): The identifier of the flow that you want to prepare. Returns: str: The status of the flow preparation """ try: # Prepare the flow. logger.info("Preparing flow ID: %s", flow_id) response = client.prepare_flow( flowIdentifier=flow_id ) status = response.get('status') while status == "Preparing": logger.info("Preparing flow ID: %s. Status %s", flow_id, status) sleep(5) response = client.get_flow( flowIdentifier=flow_id ) status = response.get('status') print(f"Flow Status: {status}") if status == "Prepared": logger.info("Finished preparing flow ID: %s. Status %s", flow_id, status) else: logger.warning("flow ID: %s not prepared. Status %s", flow_id, status) return status except ClientError as e: logger.exception("Client error preparing flow: %s", {str(e)}) raise except Exception as e: logger.exception("Unexepcted error preparing flow: %s", {str(e)}) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [PrepareFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareFlow)를 참조하세요. ------ # AWS SDK와 `UpdateFlow` 함께 사용 다음 코드 예시는 `UpdateFlow`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름을 업데이트합니다. ``` def update_flow(client, flow_id, flow_name, flow_description, role_arn, flow_def): """ Updates an Amazon Bedrock flow. Args: client: bedrock agent boto3 client. flow_id (str): The ID for the flow that you want to update. flow_name (str): The name for the flow. role_arn (str): The ARN for the IAM role that use flow uses. flow_def (json): The JSON definition of the flow that you want to create. Returns: dict: Flow information if successful. """ try: logger.info("Updating flow: %s.", flow_id) response = client.update_flow( flowIdentifier=flow_id, name=flow_name, description=flow_description, executionRoleArn=role_arn, definition=flow_def ) logger.info("Successfully updated flow: %s. ID: %s", flow_name, {response['id']}) return response except ClientError as e: logger.exception("Client error updating flow: %s", {str(e)}) raise except Exception as e: logger.exception("Unexepcted error updating flow: %s", {str(e)}) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [UpdateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateFlow)를 참조하세요. ------ # AWS SDK와 `UpdateFlowAlias` 함께 사용 다음 코드 예시는 `UpdateFlowAlias`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 흐름의 별칭을 업데이트합니다. ``` def update_flow_alias(client, flow_id, alias_id, flow_version, name, description): """ Updates an alias for an Amazon Bedrock flow. Args: client: bedrock agent boto3 client. flow_id (str): The identifier of the flow. Returns: str: The response from UpdateFlowAlias. """ try: logger.info("Updating flow alias %s for flow: %s.", alias_id, flow_id) response = client.update_flow_alias( aliasIdentifier=alias_id, flowIdentifier=flow_id, name=name, description=description, routingConfiguration=[ { "flowVersion": flow_version } ] ) logger.info("Successfully updated flow alias %s for %s.", alias_id, flow_id) return response except ClientError as e: logging.exception("Client error updating alias %s for flow: %s - %s", alias_id, flow_id, str(e)) raise except Exception as e: logging.exception("Unexpected error updating alias %s for flow : %s - %s", alias_id, flow_id, str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [UpdateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateFlowAlias)를 참조하세요. ------ # AWS SDK와 `UpdateKnowledgeBase` 함께 사용 다음 코드 예시는 `UpdateKnowledgeBase`의 사용 방법을 보여 줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. Amazon Bedrock 지식 기반을 업데이트합니다. ``` def update_knowledge_base(bedrock_agent_client, knowledge_base_id, name=None, description=None, role_arn=None): """ Updates an existing knowledge base. Args: bedrock_agent_client: The Boto3 Bedrock Agent client. knowledge_base_id (str): The ID of the knowledge base to update. name (str, optional): The new name for the knowledge base. description (str, optional): The new description for the knowledge base. role_arn (str, optional): The new IAM role ARN for the knowledge base. Returns: dict: The details of the updated knowledge base. """ try: kwargs = { "knowledgeBaseId": knowledge_base_id, "knowledgeBaseConfiguration": { "type": "VECTOR", "vectorKnowledgeBaseConfiguration": { "embeddingModelArn": "arn:aws:bedrock:us-east-1::foundation-model/amazon.titan-embed-text-v1" } } } if name: kwargs["name"] = name if description: kwargs["description"] = description if role_arn: kwargs["roleArn"] = role_arn response = bedrock_agent_client.update_knowledge_base(**kwargs) logger.info("Updated knowledge base: %s", knowledge_base_id) return response["knowledgeBase"] except ClientError as err: logger.error( "Couldn't update knowledge base %s. Here's why: %s: %s", knowledge_base_id, err.response["Error"]["Code"], err.response["Error"]["Message"], ) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 [UpdateKnowledgeBase](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/UpdateKnowledgeBase)를 참조하세요. ------ # AWS SDKs를 사용하는 Amazon Bedrock 에이전트에 대한 시나리오 다음 코드 예제에서는 Amazon Bedrock Agents AWS SDKs에서 일반적인 시나리오를 구현하는 방법을 보여줍니다. 이러한 시나리오에서는 Amazon Bedrock Agents 내에서 또는 다른 AWS 서비스와 결합된 상태에서 여러 함수를 직접적으로 호출하여 특정 작업을 수행하는 방법을 보여줍니다. 각 시나리오에는 전체 소스 코드에 대한 링크가 포함되어 있습니다. 여기에서 코드를 설정 및 실행하는 방법에 대한 지침을 찾을 수 있습니다. 시나리오는 컨텍스트에 맞는 서비스 작업을 이해하는 데 도움이 되도록 중급 수준의 경험을 대상으로 합니다. **Topics** + [흐름 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockFlows_section.md) + [관리형 프롬프트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockPrompts_section.md) + [에이전트 생성 및 간접 호출](bedrock-agent_example_bedrock-agent_GettingStartedWithBedrockAgents_section.md) + [Step Functions를 사용하여 생성형 AI 애플리케이션 오케스트레이션](bedrock-agent_example_cross_ServerlessPromptChaining_section.md) # AWS SDK를 사용하여 Amazon Bedrock 흐름을 생성하고 호출하는 방법을 보여주는 end-to-end 예제 다음 코드 예제에서는 다음과 같은 작업을 수행하는 방법을 보여줍니다. + 흐름에 대한 실행 역할을 생성합니다. + 흐름을 생성합니다. + 완전히 구성된 흐름을 배포합니다. + 사용자가 제공한 프롬프트로 흐름을 간접 호출합니다. + 생성된 모든 리소스를 삭제합니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 사용자 지정 장르 및 노래 수를 기반으로 음악 재생 목록을 생성합니다. ``` from datetime import datetime import logging import boto3 from botocore.exceptions import ClientError from roles import create_flow_role, delete_flow_role, update_role_policy from flow import create_flow, prepare_flow, delete_flow from run_flow import run_playlist_flow from flow_version import create_flow_version, delete_flow_version from flow_alias import create_flow_alias, delete_flow_alias logging.basicConfig( level=logging.INFO ) logger = logging.getLogger(__name__) def create_input_node(name): """ Creates an input node configuration for an Amazon Bedrock flow. The input node serves as the entry point for the flow and defines the initial document structure that will be passed to subsequent nodes. Args: name (str): The name of the input node. Returns: dict: The input node configuration. """ return { "type": "Input", "name": name, "outputs": [ { "name": "document", "type": "Object" } ] } def create_prompt_node(name, model_id): """ Creates a prompt node configuration for a Bedrock flow that generates music playlists. The prompt node defines an inline prompt template that creates a music playlist based on a specified genre and number of songs. The prompt uses two variables that are mapped from the input JSON object: - {{genre}}: The genre of music to create a playlist for - {{number}}: The number of songs to include in the playlist Args: name (str): The name of the prompt node. model_id (str): The identifier of the foundation model to use for the prompt. Returns: dict: The prompt node. """ return { "type": "Prompt", "name": name, "configuration": { "prompt": { "sourceConfiguration": { "inline": { "modelId": model_id, "templateType": "TEXT", "inferenceConfiguration": { "text": { "temperature": 0.8 } }, "templateConfiguration": { "text": { "text": "Make me a {{genre}} playlist consisting of the following number of songs: {{number}}." } } } } } }, "inputs": [ { "name": "genre", "type": "String", "expression": "$.data.genre" }, { "name": "number", "type": "Number", "expression": "$.data.number" } ], "outputs": [ { "name": "modelCompletion", "type": "String" } ] } def create_output_node(name): """ Creates an output node configuration for a Bedrock flow. The output node validates that the output from the last node is a string and returns it unmodified. The input name must be "document". Args: name (str): The name of the output node. Returns: dict: The output node configuration containing the output node: """ return { "type": "Output", "name": name, "inputs": [ { "name": "document", "type": "String", "expression": "$.data" } ] } def create_playlist_flow(client, flow_name, flow_description, role_arn, prompt_model_id): """ Creates the playlist generator flow. Args: client: bedrock agent boto3 client. role_arn (str): Name for the new IAM role. prompt_model_id (str): The id of the model to use in the prompt node. Returns: dict: The response from the create_flow operation. """ input_node = create_input_node("FlowInput") prompt_node = create_prompt_node("MakePlaylist", prompt_model_id) output_node = create_output_node("FlowOutput") # Create connections between the nodes connections = [] # First, create connections between the output of the flow # input node and each input of the prompt node. for prompt_node_input in prompt_node["inputs"]: connections.append( { "name": "_".join([input_node["name"], prompt_node["name"], prompt_node_input["name"]]), "source": input_node["name"], "target": prompt_node["name"], "type": "Data", "configuration": { "data": { "sourceOutput": input_node["outputs"][0]["name"], "targetInput": prompt_node_input["name"] } } } ) # Then, create a connection between the output of the prompt node and the input of the flow output node connections.append( { "name": "_".join([prompt_node["name"], output_node["name"]]), "source": prompt_node["name"], "target": output_node["name"], "type": "Data", "configuration": { "data": { "sourceOutput": prompt_node["outputs"][0]["name"], "targetInput": output_node["inputs"][0]["name"] } } } ) flow_def = { "nodes": [input_node, prompt_node, output_node], "connections": connections } # Create the flow. response = create_flow( client, flow_name, flow_description, role_arn, flow_def) return response def get_model_arn(client, model_id): """ Gets the Amazon Resource Name (ARN) for a model. Args: client (str): Amazon Bedrock boto3 client. model_id (str): The id of the model. Returns: str: The ARN of the model. """ try: # Call GetFoundationModelDetails operation response = client.get_foundation_model(modelIdentifier=model_id) # Extract model ARN from the response model_arn = response['modelDetails']['modelArn'] return model_arn except ClientError as e: logger.exception("Client error getting model ARN: %s", {str(e)}) raise except Exception as e: logger.exception("Unexpected error getting model ARN: %s", {str(e)}) raise def prepare_flow_version_and_alias(bedrock_agent_client, flow_id): """ Prepares the flow and then creates a flow version and flow alias. Args: bedrock_agent_client: Amazon Bedrock Agent boto3 client. flowd_id (str): The ID of the flow that you want to prepare. Returns: The flow_version and flow_alias. """ status = prepare_flow(bedrock_agent_client, flow_id) flow_version = None flow_alias = None if status == 'Prepared': # Create the flow version and alias. flow_version = create_flow_version(bedrock_agent_client, flow_id, f"flow version for flow {flow_id}.") flow_alias = create_flow_alias(bedrock_agent_client, flow_id, flow_version, "latest", f"Alias for flow {flow_id}, version {flow_version}") return flow_version, flow_alias def delete_role_resources(bedrock_agent_client, iam_client, role_name, flow_id, flow_version, flow_alias): """ Deletes the flow, flow alias, flow version, and IAM roles. Args: bedrock_agent_client: Amazon Bedrock Agent boto3 client. iam_client: Amazon IAM boto3 client. role_name (str): The name of the IAM role. flow_id (str): The id of the flow. flow_version (str): The version of the flow. flow_alias (str): The alias of the flow. """ if flow_id is not None: if flow_alias is not None: delete_flow_alias(bedrock_agent_client, flow_id, flow_alias) if flow_version is not None: delete_flow_version(bedrock_agent_client, flow_id, flow_version) delete_flow(bedrock_agent_client, flow_id) if role_name is not None: delete_flow_role(iam_client, role_name) def main(): """ Creates, runs, and optionally deletes a Bedrock flow for generating music playlists. Note: Requires valid AWS credentials in the default profile """ delete_choice = "y" try: # Get various boto3 clients. session = boto3.Session(profile_name='default') bedrock_agent_runtime_client = session.client('bedrock-agent-runtime') bedrock_agent_client = session.client('bedrock-agent') bedrock_client = session.client('bedrock') iam_client = session.client('iam') role_name = None flow_id = None flow_version = None flow_alias = None #Change the model as needed. prompt_model_id = "amazon.nova-pro-v1:0" # Base the flow name on the current date and time current_time = datetime.now() timestamp = current_time.strftime("%Y-%m-%d-%H-%M-%S") flow_name = f"FlowPlayList_{timestamp}" flow_description = "A flow to generate a music playlist." # Create a role for the flow. role_name = f"BedrockFlowRole-{flow_name}" role = create_flow_role(iam_client, role_name) role_arn = role['Arn'] # Create the flow. response = create_playlist_flow( bedrock_agent_client, flow_name, flow_description, role_arn, prompt_model_id) flow_id = response.get('id') if flow_id: # Update accessible resources in the role. model_arn = get_model_arn(bedrock_client, prompt_model_id) update_role_policy(iam_client, role_name, [ response.get('arn'), model_arn]) # Prepare the flow and flow version. flow_version, flow_alias = prepare_flow_version_and_alias( bedrock_agent_client, flow_id) # Run the flow. if flow_version and flow_alias: run_playlist_flow(bedrock_agent_runtime_client, flow_id, flow_alias) delete_choice = input("Delete flow? y or n : ").lower() else: print("Couldn't run. Deleting flow and role.") delete_flow(bedrock_agent_client, flow_id) delete_flow_role(iam_client, role_name) else: print("Couldn't create flow.") except Exception as e: print(f"Fatal error: {str(e)}") finally: if delete_choice == 'y': delete_role_resources(bedrock_agent_client, iam_client, role_name, flow_id, flow_version, flow_alias) else: print("Flow not deleted. ") print(f"\tFlow ID: {flow_id}") print(f"\tFlow version: {flow_version}") print(f"\tFlow alias: {flow_alias}") print(f"\tRole ARN: {role_arn}") print("Done!") if __name__ == "__main__": main() def invoke_flow(client, flow_id, flow_alias_id, input_data): """ Invoke an Amazon Bedrock flow and handle the response stream. Args: client: Boto3 client for Amazon Bedrock agent runtime. flow_id: The ID of the flow to invoke. flow_alias_id: The alias ID of the flow. input_data: Input data for the flow. Returns: Dict containing flow status and flow output. """ response = None request_params = None request_params = { "flowIdentifier": flow_id, "flowAliasIdentifier": flow_alias_id, "inputs": [input_data], "enableTrace": True } response = client.invoke_flow(**request_params) flow_status = "" output= "" # Process the streaming response for event in response['responseStream']: # Check if flow is complete. if 'flowCompletionEvent' in event: flow_status = event['flowCompletionEvent']['completionReason'] # Save the model output. elif 'flowOutputEvent' in event: output = event['flowOutputEvent']['content']['document'] logger.info("Output : %s", output) # Log trace events. elif 'flowTraceEvent' in event: logger.info("Flow trace: %s", event['flowTraceEvent']) return { "flow_status": flow_status, "output": output } def run_playlist_flow(bedrock_agent_client, flow_id, flow_alias_id): """ Runs the playlist generator flow. Args: bedrock_agent_client: Boto3 client for Amazon Bedrock agent runtime. flow_id: The ID of the flow to run. flow_alias_id: The alias ID of the flow. """ print ("Welcome to the playlist generator flow.") # Get the initial prompt from the user. genre = input("Enter genre: ") number_of_songs = int(input("Enter number of songs: ")) # Use prompt to create input data for the input node. flow_input_data = { "content": { "document": { "genre" : genre, "number" : number_of_songs } }, "nodeName": "FlowInput", "nodeOutputName": "document" } try: result = invoke_flow( bedrock_agent_client, flow_id, flow_alias_id, flow_input_data) status = result['flow_status'] if status == "SUCCESS": # The flow completed successfully. logger.info("The flow %s successfully completed.", flow_id) print(result['output']) else: logger.warning("Flow status: %s",status) except ClientError as e: print(f"Client error: {str(e)}") logger.error("Client error: %s", {str(e)}) raise except Exception as e: logger.error("An error occurred: %s", {str(e)}) logger.error("Error type: %s", {type(e)}) raise def create_flow_role(client, role_name): """ Creates an IAM role for Amazon Bedrock with permissions to run a flow. Args: role_name (str): Name for the new IAM role. Returns: str: The role Amazon Resource Name. """ # Trust relationship policy - allows Amazon Bedrock service to assume this role. trust_policy = { "Version":"2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": "bedrock.amazonaws.com" }, "Action": "sts:AssumeRole" }] } # Basic inline policy for for running a flow. resources = "*" bedrock_policy = { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:InvokeModel", "bedrock:Retrieve", "bedrock:RetrieveAndGenerate" ], # Using * as placeholder - Later you update with specific ARNs. "Resource": resources } ] } try: # Create the IAM role with trust policy logging.info("Creating role: %s",role_name) role = client.create_role( RoleName=role_name, AssumeRolePolicyDocument=json.dumps(trust_policy), Description="Role for Amazon Bedrock operations" ) # Attach inline policy to the role print("Attaching inline policy") client.put_role_policy( RoleName=role_name, PolicyName=f"{role_name}-policy", PolicyDocument=json.dumps(bedrock_policy) ) logging.info("Create Role ARN: %s", role['Role']['Arn']) return role['Role'] except ClientError as e: logging.warning("Error creating role: %s", str(e)) raise except Exception as e: logging.warning("Unexpected error: %s", str(e)) raise def update_role_policy(client, role_name, resource_arns): """ Updates an IAM role's inline policy with specific resource ARNs. Args: role_name (str): Name of the existing role. resource_arns (list): List of resource ARNs to allow access to. """ updated_policy = { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "bedrock:GetFlow", "bedrock:InvokeModel", "bedrock:Retrieve", "bedrock:RetrieveAndGenerate" ], "Resource": resource_arns } ] } try: client.put_role_policy( RoleName=role_name, PolicyName=f"{role_name}-policy", PolicyDocument=json.dumps(updated_policy) ) logging.info("Updated policy for role: %s",role_name) except ClientError as e: logging.warning("Error updating role policy: %s", str(e)) raise def delete_flow_role(client, role_name): """ Deletes an IAM role. Args: role_name (str): Name of the role to delete. """ try: # Detach and delete inline policies policies = client.list_role_policies(RoleName=role_name)['PolicyNames'] for policy_name in policies: client.delete_role_policy(RoleName=role_name, PolicyName=policy_name) # Delete the role client.delete_role(RoleName=role_name) logging.info("Deleted role: %s", role_name) except ClientError as e: logging.info("Error Deleting role: %s", str(e)) raise ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 다음 주제를 참조하세요. + [CreateFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlow) + [CreateFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowAlias) + [CreateFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateFlowVersion) + [DeleteFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlow) + [DeleteFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowAlias) + [DeleteFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteFlowVersion) + [GetFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlow) + [GetFlowAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowAlias) + [GetFlowVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetFlowVersion) + [InvokeFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-runtime-2023-12-12/InvokeFlow) + [PrepareFlow](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareFlow) ------ # AWS SDK를 사용하여 Amazon Bedrock 관리형 프롬프트를 생성하고 호출하는 방법을 보여주는 end-to-end 예제 다음 코드 예제에서는 다음과 같은 작업을 수행하는 방법을 보여줍니다. + 관리형 프롬프트를 만듭니다. + 프롬프트 버전을 만듭니다. + 이 버전을 사용하여 프롬프트를 간접 호출합니다. + 리소스를 정리합니다(선택 사항). ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 관리형 프롬프트를 만들고 간접 호출합니다. ``` import argparse import boto3 import logging import time # Now import the modules from prompt import create_prompt, create_prompt_version, delete_prompt from run_prompt import invoke_prompt logging.basicConfig( level=logging.INFO, format='%(levelname)s: %(message)s' ) logger = logging.getLogger(__name__) def run_scenario(bedrock_client, bedrock_runtime_client, model_id, cleanup=True): """ Runs the Amazon Bedrock managed prompt scenario. Args: bedrock_client: The Amazon Bedrock Agent client. bedrock_runtime_client: The Amazon Bedrock Runtime client. model_id (str): The model ID to use for the prompt. cleanup (bool): Whether to clean up resources at the end of the scenario. Returns: dict: A dictionary containing the created resources. """ prompt_id = None try: # Step 1: Create a prompt print("\n=== Step 1: Creating a prompt ===") prompt_name = f"PlaylistGenerator-{int(time.time())}" prompt_description = "Playlist generator" prompt_template = """ Make me a {{genre}} playlist consisting of the following number of songs: {{number}}.""" create_response = create_prompt( bedrock_client, prompt_name, prompt_description, prompt_template, model_id ) prompt_id = create_response['id'] print(f"Created prompt: {prompt_name} with ID: {prompt_id}") # Create a version of the prompt print("\n=== Creating a version of the prompt ===") version_response = create_prompt_version( bedrock_client, prompt_id, description="Initial version of the product description generator" ) prompt_version_arn = version_response['arn'] prompt_version = version_response['version'] print(f"Created prompt version: {prompt_version}") print(f"Prompt version ARN: {prompt_version_arn}") # Step 2: Invoke the prompt directly print("\n=== Step 2: Invoking the prompt ===") input_variables = { "genre": "pop", "number": "2", } # Use the ARN from the create_prompt_version response result = invoke_prompt( bedrock_runtime_client, prompt_version_arn, input_variables ) # Display the playlist print(f"\n{result}") # Step 3: Clean up resources (optional) if cleanup: print("\n=== Step 3: Cleaning up resources ===") # Delete the prompt print(f"Deleting prompt {prompt_id}...") delete_prompt(bedrock_client, prompt_id) print("Cleanup complete") else: print("\n=== Resources were not cleaned up ===") print(f"Prompt ID: {prompt_id}") except Exception as e: logger.exception("Error in scenario: %s", str(e)) # Attempt to clean up if an error occurred and cleanup was requested if cleanup and prompt_id: try: print("\nCleaning up resources after error...") # Delete the prompt try: delete_prompt(bedrock_client, prompt_id) print("Cleanup after error complete") except Exception as cleanup_error: logger.error("Error during cleanup: %s", str(cleanup_error)) except Exception as final_error: logger.error("Final error during cleanup: %s", str(final_error)) # Re-raise the original exception raise def main(): """ Entry point for the Amazon Bedrock managed prompt scenario. """ parser = argparse.ArgumentParser( description="Run the Amazon Bedrock managed prompt scenario." ) parser.add_argument( '--region', default='us-east-1', help="The AWS Region to use." ) parser.add_argument( '--model-id', default='anthropic.claude-v2', help="The model ID to use for the prompt." ) parser.add_argument( '--cleanup', action='store_true', default=True, help="Clean up resources at the end of the scenario." ) parser.add_argument( '--no-cleanup', action='store_false', dest='cleanup', help="Don't clean up resources at the end of the scenario." ) args = parser.parse_args() bedrock_client = boto3.client('bedrock-agent', region_name=args.region) bedrock_runtime_client = boto3.client('bedrock-runtime', region_name=args.region) print("=== Amazon Bedrock Managed Prompt Scenario ===") print(f"Region: {args.region}") print(f"Model ID: {args.model_id}") print(f"Cleanup resources: {args.cleanup}") try: run_scenario( bedrock_client, bedrock_runtime_client, args.model_id, args.cleanup ) except Exception as e: logger.exception("Error running scenario: %s", str(e)) if __name__ == "__main__": main() ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 다음 주제를 참조하세요. + [Converse](https://docs.aws.amazon.com/goto/boto3/bedrock-runtime-2023-09-30/Converse) + [CreatePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePrompt) + [CreatePromptVersion](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreatePromptVersion) + [DeletePrompt](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeletePrompt) ------ # AWS SDK를 사용하여 Amazon Bedrock Agents를 생성하고 호출하는 방법을 보여주는 end-to-end 예제 다음 코드 예제에서는 다음과 같은 작업을 수행하는 방법을 보여줍니다. + 에이전트에 대한 실행 역할을 생성합니다. + 에이전트를 생성하고 DRAFT 버전을 배포합니다. + 에이전트의 기능을 구현하는 Lambda 함수를 생성합니다. + 에이전트를 Lambda 함수에 연결하는 작업 그룹을 생성합니다. + 완전히 구성된 에이전트를 배포합니다. + 사용자가 제공한 프롬프트로 에이전트를 간접 호출합니다. + 생성된 모든 리소스를 삭제합니다. ------ #### [ Python ] **SDK for Python(Boto3)** GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/bedrock-agent#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요. 에이전트를 생성 및 간접 호출합니다. ``` REGION = "us-east-1" ROLE_POLICY_NAME = "agent_permissions" class BedrockAgentScenarioWrapper: """Runs a scenario that shows how to get started using Amazon Bedrock Agents.""" def __init__( self, bedrock_agent_client, runtime_client, lambda_client, iam_resource, postfix ): self.iam_resource = iam_resource self.lambda_client = lambda_client self.bedrock_agent_runtime_client = runtime_client self.postfix = postfix self.bedrock_wrapper = BedrockAgentWrapper(bedrock_agent_client) self.agent = None self.agent_alias = None self.agent_role = None self.prepared_agent_details = None self.lambda_role = None self.lambda_function = None def run_scenario(self): print("=" * 88) print("Welcome to the Amazon Bedrock Agents demo.") print("=" * 88) # Query input from user print("Let's start with creating an agent:") print("-" * 40) name, foundation_model = self._request_name_and_model_from_user() print("-" * 40) # Create an execution role for the agent self.agent_role = self._create_agent_role(foundation_model) # Create the agent self.agent = self._create_agent(name, foundation_model) # Prepare a DRAFT version of the agent self.prepared_agent_details = self._prepare_agent() # Create the agent's Lambda function self.lambda_function = self._create_lambda_function() # Configure permissions for the agent to invoke the Lambda function self._allow_agent_to_invoke_function() self._let_function_accept_invocations_from_agent() # Create an action group to connect the agent with the Lambda function self._create_agent_action_group() # If the agent has been modified or any components have been added, prepare the agent again components = [self._get_agent()] components += self._get_agent_action_groups() components += self._get_agent_knowledge_bases() latest_update = max(component["updatedAt"] for component in components) if latest_update > self.prepared_agent_details["preparedAt"]: self.prepared_agent_details = self._prepare_agent() # Create an agent alias self.agent_alias = self._create_agent_alias() # Test the agent self._chat_with_agent(self.agent_alias) print("=" * 88) print("Thanks for running the demo!\n") if q.ask("Do you want to delete the created resources? [y/N] ", q.is_yesno): self._delete_resources() print("=" * 88) print( "All demo resources have been deleted. Thanks again for running the demo!" ) else: self._list_resources() print("=" * 88) print("Thanks again for running the demo!") def _request_name_and_model_from_user(self): existing_agent_names = [ agent["agentName"] for agent in self.bedrock_wrapper.list_agents() ] while True: name = q.ask("Enter an agent name: ", self.is_valid_agent_name) if name.lower() not in [n.lower() for n in existing_agent_names]: break print( f"Agent {name} conflicts with an existing agent. Please use a different name." ) models = ["anthropic.claude-instant-v1", "anthropic.claude-v2"] model_id = models[ q.choose("Which foundation model would you like to use? ", models) ] return name, model_id def _create_agent_role(self, model_id): role_name = f"AmazonBedrockExecutionRoleForAgents_{self.postfix}" model_arn = f"arn:aws:bedrock:{REGION}::foundation-model/{model_id}*" print("Creating an an execution role for the agent...") try: role = self.iam_resource.create_role( RoleName=role_name, AssumeRolePolicyDocument=json.dumps( { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "bedrock.amazonaws.com"}, "Action": "sts:AssumeRole", } ], } ), ) role.Policy(ROLE_POLICY_NAME).put( PolicyDocument=json.dumps( { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "bedrock:InvokeModel", "Resource": model_arn, } ], } ) ) except ClientError as e: logger.error(f"Couldn't create role {role_name}. Here's why: {e}") raise return role def _create_agent(self, name, model_id): print("Creating the agent...") instruction = """ You are a friendly chat bot. You have access to a function called that returns information about the current date and time. When responding with date or time, please make sure to add the timezone UTC. """ agent = self.bedrock_wrapper.create_agent( agent_name=name, foundation_model=model_id, instruction=instruction, role_arn=self.agent_role.arn, ) self._wait_for_agent_status(agent["agentId"], "NOT_PREPARED") return agent def _prepare_agent(self): print("Preparing the agent...") agent_id = self.agent["agentId"] prepared_agent_details = self.bedrock_wrapper.prepare_agent(agent_id) self._wait_for_agent_status(agent_id, "PREPARED") return prepared_agent_details def _create_lambda_function(self): print("Creating the Lambda function...") function_name = f"AmazonBedrockExampleFunction_{self.postfix}" self.lambda_role = self._create_lambda_role() try: deployment_package = self._create_deployment_package(function_name) lambda_function = self.lambda_client.create_function( FunctionName=function_name, Description="Lambda function for Amazon Bedrock example", Runtime="python3.11", Role=self.lambda_role.arn, Handler=f"{function_name}.lambda_handler", Code={"ZipFile": deployment_package}, Publish=True, ) waiter = self.lambda_client.get_waiter("function_active_v2") waiter.wait(FunctionName=function_name) except ClientError as e: logger.error( f"Couldn't create Lambda function {function_name}. Here's why: {e}" ) raise return lambda_function def _create_lambda_role(self): print("Creating an execution role for the Lambda function...") role_name = f"AmazonBedrockExecutionRoleForLambda_{self.postfix}" try: role = self.iam_resource.create_role( RoleName=role_name, AssumeRolePolicyDocument=json.dumps( { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": {"Service": "lambda.amazonaws.com"}, "Action": "sts:AssumeRole", } ], } ), ) role.attach_policy( PolicyArn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) print(f"Created role {role_name}") except ClientError as e: logger.error(f"Couldn't create role {role_name}. Here's why: {e}") raise print("Waiting for the execution role to be fully propagated...") wait(10) return role def _allow_agent_to_invoke_function(self): policy = self.iam_resource.RolePolicy( self.agent_role.role_name, ROLE_POLICY_NAME ) doc = policy.policy_document doc["Statement"].append( { "Effect": "Allow", "Action": "lambda:InvokeFunction", "Resource": self.lambda_function["FunctionArn"], } ) self.agent_role.Policy(ROLE_POLICY_NAME).put(PolicyDocument=json.dumps(doc)) def _let_function_accept_invocations_from_agent(self): try: self.lambda_client.add_permission( FunctionName=self.lambda_function["FunctionName"], SourceArn=self.agent["agentArn"], StatementId="BedrockAccess", Action="lambda:InvokeFunction", Principal="bedrock.amazonaws.com", ) except ClientError as e: logger.error( f"Couldn't grant Bedrock permission to invoke the Lambda function. Here's why: {e}" ) raise def _create_agent_action_group(self): print("Creating an action group for the agent...") try: with open("./scenario_resources/api_schema.yaml") as file: self.bedrock_wrapper.create_agent_action_group( name="current_date_and_time", description="Gets the current date and time.", agent_id=self.agent["agentId"], agent_version=self.prepared_agent_details["agentVersion"], function_arn=self.lambda_function["FunctionArn"], api_schema=json.dumps(yaml.safe_load(file)), ) except ClientError as e: logger.error(f"Couldn't create agent action group. Here's why: {e}") raise def _get_agent(self): return self.bedrock_wrapper.get_agent(self.agent["agentId"]) def _get_agent_action_groups(self): return self.bedrock_wrapper.list_agent_action_groups( self.agent["agentId"], self.prepared_agent_details["agentVersion"] ) def _get_agent_knowledge_bases(self): return self.bedrock_wrapper.list_agent_knowledge_bases( self.agent["agentId"], self.prepared_agent_details["agentVersion"] ) def _create_agent_alias(self): print("Creating an agent alias...") agent_alias_name = "test_agent_alias" agent_alias = self.bedrock_wrapper.create_agent_alias( agent_alias_name, self.agent["agentId"] ) self._wait_for_agent_status(self.agent["agentId"], "PREPARED") return agent_alias def _wait_for_agent_status(self, agent_id, status): while self.bedrock_wrapper.get_agent(agent_id)["agentStatus"] != status: wait(2) def _chat_with_agent(self, agent_alias): print("-" * 88) print("The agent is ready to chat.") print("Try asking for the date or time. Type 'exit' to quit.") # Create a unique session ID for the conversation session_id = uuid.uuid4().hex while True: prompt = q.ask("Prompt: ", q.non_empty) if prompt == "exit": break response = asyncio.run(self._invoke_agent(agent_alias, prompt, session_id)) print(f"Agent: {response}") async def _invoke_agent(self, agent_alias, prompt, session_id): response = self.bedrock_agent_runtime_client.invoke_agent( agentId=self.agent["agentId"], agentAliasId=agent_alias["agentAliasId"], sessionId=session_id, inputText=prompt, ) completion = "" for event in response.get("completion"): chunk = event["chunk"] completion += chunk["bytes"].decode() return completion def _delete_resources(self): if self.agent: agent_id = self.agent["agentId"] if self.agent_alias: agent_alias_id = self.agent_alias["agentAliasId"] print("Deleting agent alias...") self.bedrock_wrapper.delete_agent_alias(agent_id, agent_alias_id) print("Deleting agent...") agent_status = self.bedrock_wrapper.delete_agent(agent_id)["agentStatus"] while agent_status == "DELETING": wait(5) try: agent_status = self.bedrock_wrapper.get_agent( agent_id, log_error=False )["agentStatus"] except ClientError as err: if err.response["Error"]["Code"] == "ResourceNotFoundException": agent_status = "DELETED" if self.lambda_function: name = self.lambda_function["FunctionName"] print(f"Deleting function '{name}'...") self.lambda_client.delete_function(FunctionName=name) if self.agent_role: print(f"Deleting role '{self.agent_role.role_name}'...") self.agent_role.Policy(ROLE_POLICY_NAME).delete() self.agent_role.delete() if self.lambda_role: print(f"Deleting role '{self.lambda_role.role_name}'...") for policy in self.lambda_role.attached_policies.all(): policy.detach_role(RoleName=self.lambda_role.role_name) self.lambda_role.delete() def _list_resources(self): print("-" * 40) print(f"Here is the list of created resources in '{REGION}'.") print("Make sure you delete them once you're done to avoid unnecessary costs.") if self.agent: print(f"Bedrock Agent: {self.agent['agentName']}") if self.lambda_function: print(f"Lambda function: {self.lambda_function['FunctionName']}") if self.agent_role: print(f"IAM role: {self.agent_role.role_name}") if self.lambda_role: print(f"IAM role: {self.lambda_role.role_name}") @staticmethod def is_valid_agent_name(answer): valid_regex = r"^[a-zA-Z0-9_-]{1,100}$" return ( answer if answer and len(answer) <= 100 and re.match(valid_regex, answer) else None, "I need a name for the agent, please. Valid characters are a-z, A-Z, 0-9, _ (underscore) and - (hyphen).", ) @staticmethod def _create_deployment_package(function_name): buffer = io.BytesIO() with zipfile.ZipFile(buffer, "w") as zipped: zipped.write( "./scenario_resources/lambda_function.py", f"{function_name}.py" ) buffer.seek(0) return buffer.read() if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") postfix = "".join( random.choice(string.ascii_lowercase + "0123456789") for _ in range(8) ) scenario = BedrockAgentScenarioWrapper( bedrock_agent_client=boto3.client( service_name="bedrock-agent", region_name=REGION ), runtime_client=boto3.client( service_name="bedrock-agent-runtime", region_name=REGION ), lambda_client=boto3.client(service_name="lambda", region_name=REGION), iam_resource=boto3.resource("iam"), postfix=postfix, ) try: scenario.run_scenario() except Exception as e: logging.exception(f"Something went wrong with the demo. Here's what: {e}") ``` + API 세부 정보는 *AWS SDK for Python (Boto3) API 참조*의 다음 주제를 참조하세요. + [CreateAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgent) + [CreateAgentActionGroup](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentActionGroup) + [CreateAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/CreateAgentAlias) + [DeleteAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgent) + [DeleteAgentAlias](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/DeleteAgentAlias) + [GetAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/GetAgent) + [ListAgentActionGroups](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentActionGroups) + [ListAgentKnowledgeBases](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgentKnowledgeBases) + [ListAgents](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/ListAgents) + [PrepareAgent](https://docs.aws.amazon.com/goto/boto3/bedrock-agent-2023-12-12/PrepareAgent) ------ # Amazon Bedrock 및 Step Functions를 사용한 생성형 AI 애플리케이션 구축 및 오케스트레이션 다음 코드 예제는 Amazon Bedrock 및 Step Functions를 사용하여 생성형 AI 애플리케이션을 구축하고 오케스트레이션하는 방법을 보여줍니다. ------ #### [ Python ] **SDK for Python(Boto3)** Amazon Bedrock 서버리스 프롬프트 체이닝 시나리오는 [AWS Step Functions](https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html), [Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) 및 [https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html)의 방법을 사용하여 복잡하고 확장성이 뛰어난 서버리스 생성형 AI 애플리케이션을 구축하고 오케스트레이션하는 방법을 보여줍니다. 여기에는 다음과 같은 작업 예제가 포함됩니다. + 문학 블로그에 특정 소설에 대한 분석을 작성합니다. 이 예제에서는 간단하고 순차적인 프롬프트 체인을 보여줍니다. + 주어진 주제에 대한 짧은 스토리를 생성합니다. 이 예제에서는 AI가 이전에 생성한 항목 목록을 어떻게 반복적으로 처리하는지 보여줍니다. + 주어진 목적지로 향하는 주말 휴가 일정을 생성합니다. 이 예제에서는 여러 개의 고유한 프롬프트를 병렬화하는 방법을 보여줍니다. + 영화 프로듀서인 사용자에게 영화 아이디어를 피칭합니다. 이 예제에서는 동일한 프롬프트를 서로 다른 추론 파라미터와 병렬화하는 방법, 체인의 이전 단계로 역추적하는 방법, 워크플로의 일부로 사람의 입력을 포함하는 방법을 보여줍니다. + 사용자가 가진 재료를 바탕으로 식사를 계획합니다. 이 예제에서는 프롬프트 체인이 두 개의 개별 AI 대화를 어떻게 통합하는지 보여줍니다. 두 AI 페르소나가 최종 결과를 개선하기 위해 서로 토론합니다. + 요즘 가장 화제가 되는 GitHub 리포지토리를 찾아 요약합니다. 이 예제에서는 외부 API와 상호 작용하는 여러 AI 에이전트를 연결하는 방법을 보여줍니다. 전체 소스 코드와 설정 및 실행 방법에 대한 지침은 [GitHub](https://github.com/aws-samples/amazon-bedrock-serverless-prompt-chaining)에서 전체 프로젝트를 참조하세요. **이 예제에서 사용되는 서비스** + Amazon Bedrock + Amazon Bedrock 런타임 + Amazon Bedrock Agents + Amazon Bedrock Agents Runtime + 단계 함수 ------